In this work, we introduce a self-adaptive task farm for computational grids which is based on a single-round scheduling algorithm called dynamic deal. In principle, the dynamic deal approach employs skeletal forecasting information to automatically instrument the task farm scheduling and determine the amount of work assigned to each worker at execution time, allowing the farm to adapt effectively to different load and network conditions in the grid. In practice, it uses self-generated predictive execution values and maps tasks onto the different nodes in a single-round. The effectiveness of this approach is illustrated with a computational biology parameter sweep in a non-dedicated departmental grid.